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Nikhil Bansal is a prominent researcher in Theoretical Computer Science, focusing on the design and analysis of algorithms and discrete optimization problems. His research interests extend into related areas such as discrete mathematics, complexity theory, and machine learning, particularly emphasizing probabilistic methods. He has made significant contributions to the field, including work on potential-function proofs and gradient methods. Bansal has authored notable surveys and chapters, including a chapter on algorithmic aspects of discrepancy theory. His teaching portfolio includes courses such as Randomized Algorithms and Advanced Semidefinite Programming, showcasing his commitment to educating the next generation of computer scientists. Bansal has served on various editorial boards including the Journal of the ACM and has been involved in numerous program committees for top-tier conferences, emphasizing his active engagement in the academic community. He has mentored many graduate students and postdoctoral researchers, contributing to the training of future leaders in the field.
Department of Electrical Engineering and Computer Science